| wbsts_wrapper | R Documentation |
Wraps wbsts::wbs.lsw() (Korkas and Fryzlewicz): wild binary
segmentation applied to the locally stationary wavelet spectrum, so it
detects changes in the second-order structure (variance and
autocovariance) of a nonstationary series. Where wbs looks for
jumps in the level, this looks for jumps in how the series behaves.
wbsts_wrapper(
x,
n_intervals = 0,
cstar = 0.75,
lambda = 0.75,
scales = NULL,
seed = NULL,
...
)
x |
A numeric vector. |
n_intervals |
Number of random intervals ( |
cstar |
The unbalancedness parameter |
lambda |
How many wavelet scales are used when |
scales |
Wavelet scales to use: at least two different whole
numbers from 1 to |
seed |
Optional seed. The seed is scoped to this call:
|
... |
Additional arguments passed to |
A ggcpt object with change_in = "var".
korkas2017wbstsggchangepoint
Other changepoint engines:
bcp_wrapper(),
beast_wrapper(),
bfast_wrapper(),
binsegrcpp_wrapper(),
bocpd_wrapper(),
capa_wrapper(),
cpm_wrapper(),
cpop_wrapper(),
cpt_wrapper(),
decafs_wrapper(),
ecp_wrapper(),
envcpt_wrapper(),
esac_wrapper(),
fabisearch_wrapper(),
fastcpd_wrapper(),
fcov_wrapper(),
fmean_wrapper(),
fpop_wrapper(),
geomcp_wrapper(),
hdcov_wrapper(),
hdreg_wrapper(),
idetect_wrapper(),
inspect_wrapper(),
kcp_wrapper(),
kwc_wrapper(),
mcp_wrapper(),
mosum_wrapper(),
network_wrapper(),
not_wrapper(),
npmojo_wrapper(),
nsp_wrapper(),
ocd_wrapper(),
pilliat_wrapper(),
segmented_wrapper(),
smuce_wrapper(),
sn_wrapper(),
strucchange_wrapper(),
taylor_wrapper(),
tguh_wrapper(),
trend_wrapper(),
var_wrapper(),
wbs2_wrapper(),
wbs_wrapper()
set.seed(2026)
y <- c(as.numeric(stats::arima.sim(list(ar = 0.1), 250)),
as.numeric(stats::arima.sim(list(ar = 0.9), 250)))
wbsts_wrapper(y)
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